Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations169
Missing cells578
Missing cells (%)11.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.6 KiB
Average record size in memory209.8 B

Variable types

Text13
DateTime1
Numeric11
Boolean4

Alerts

hidr_deriv_petr_ug_l has constant value "<0.10"Constant
colif_fecales_ufc_100ml is highly overall correlated with escher_coli_ufc_100mlHigh correlation
enteroc_ufc_100ml is highly overall correlated with escher_coli_ufc_100mlHigh correlation
escher_coli_ufc_100ml is highly overall correlated with colif_fecales_ufc_100ml and 1 other fieldsHigh correlation
ica is highly overall correlated with oloresHigh correlation
olores is highly overall correlated with icaHigh correlation
tem_agua is highly overall correlated with tem_aireHigh correlation
tem_aire is highly overall correlated with tem_aguaHigh correlation
olores is highly imbalanced (62.4%)Imbalance
color is highly imbalanced (67.4%)Imbalance
espumas is highly imbalanced (79.0%)Imbalance
tem_agua has 31 (18.3%) missing valuesMissing
tem_aire has 27 (16.0%) missing valuesMissing
od has 36 (21.3%) missing valuesMissing
ph has 44 (26.0%) missing valuesMissing
olores has 18 (10.7%) missing valuesMissing
color has 18 (10.7%) missing valuesMissing
espumas has 18 (10.7%) missing valuesMissing
mat_susp has 18 (10.7%) missing valuesMissing
colif_fecales_ufc_100ml has 25 (14.8%) missing valuesMissing
escher_coli_ufc_100ml has 25 (14.8%) missing valuesMissing
enteroc_ufc_100ml has 25 (14.8%) missing valuesMissing
nitrato_mg_l has 26 (15.4%) missing valuesMissing
nh4_mg_l has 18 (10.7%) missing valuesMissing
p_total_l_mg_l has 19 (11.2%) missing valuesMissing
fosf_ortofos_mg_l has 18 (10.7%) missing valuesMissing
dbo_mg_l has 19 (11.2%) missing valuesMissing
dqo_mg_l has 18 (10.7%) missing valuesMissing
turbiedad_ntu has 18 (10.7%) missing valuesMissing
hidr_deriv_petr_ug_l has 19 (11.2%) missing valuesMissing
cr_total_mg_l has 18 (10.7%) missing valuesMissing
cd_total_mg_l has 18 (10.7%) missing valuesMissing
clorofila_a_ug_l has 17 (10.1%) missing valuesMissing
microcistina_ug_l has 21 (12.4%) missing valuesMissing
ica has 32 (18.9%) missing valuesMissing
calidad_de_agua has 32 (18.9%) missing valuesMissing

Reproduction

Analysis started2024-10-07 16:14:18.158007
Analysis finished2024-10-07 16:14:38.472971
Duration20.31 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

sitios
Text

Distinct43
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:38.776647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length41
Median length27
Mean length23.343195
Min length8

Characters and Unicode

Total characters3945
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowCanal Villanueva y Río Luján
2nd rowCanal Villanueva y Río Luján
3rd rowCanal Villanueva y Río Luján
4th rowCanal Villanueva y Río Luján
5th rowRío Lujan y Arroyo Caraguatá
ValueCountFrequency (%)
y 40
 
5.8%
río 36
 
5.3%
de 32
 
4.7%
arroyo 24
 
3.5%
espigón 16
 
2.3%
lujan 16
 
2.3%
canal 12
 
1.8%
la 12
 
1.8%
playa 12
 
1.8%
reserva 12
 
1.8%
Other values (98) 473
69.1%
2024-10-07T13:14:39.318345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
516
 
13.1%
a 481
 
12.2%
o 316
 
8.0%
e 232
 
5.9%
r 226
 
5.7%
l 194
 
4.9%
n 192
 
4.9%
i 161
 
4.1%
s 122
 
3.1%
c 111
 
2.8%
Other values (47) 1394
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
516
 
13.1%
a 481
 
12.2%
o 316
 
8.0%
e 232
 
5.9%
r 226
 
5.7%
l 194
 
4.9%
n 192
 
4.9%
i 161
 
4.1%
s 122
 
3.1%
c 111
 
2.8%
Other values (47) 1394
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
516
 
13.1%
a 481
 
12.2%
o 316
 
8.0%
e 232
 
5.9%
r 226
 
5.7%
l 194
 
4.9%
n 192
 
4.9%
i 161
 
4.1%
s 122
 
3.1%
c 111
 
2.8%
Other values (47) 1394
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
516
 
13.1%
a 481
 
12.2%
o 316
 
8.0%
e 232
 
5.9%
r 226
 
5.7%
l 194
 
4.9%
n 192
 
4.9%
i 161
 
4.1%
s 122
 
3.1%
c 111
 
2.8%
Other values (47) 1394
35.3%

codigo
Text

Distinct44
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:39.631803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0532544
Min length5

Characters and Unicode

Total characters854
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st rowTI001
2nd rowTI001
3rd rowTI001
4th rowTI001
5th rowTI006
ValueCountFrequency (%)
ti001 4
 
2.4%
ti006 4
 
2.4%
ti002 4
 
2.4%
ti003 4
 
2.4%
ti004 4
 
2.4%
ti005 4
 
2.4%
ti007 4
 
2.4%
ti008 4
 
2.4%
ti009 4
 
2.4%
sf015 4
 
2.4%
Other values (34) 129
76.3%
2024-10-07T13:14:40.168624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 206
24.1%
I 52
 
6.1%
2 44
 
5.2%
4 40
 
4.7%
3 40
 
4.7%
S 40
 
4.7%
1 37
 
4.3%
A 37
 
4.3%
T 36
 
4.2%
5 36
 
4.2%
Other values (23) 286
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 206
24.1%
I 52
 
6.1%
2 44
 
5.2%
4 40
 
4.7%
3 40
 
4.7%
S 40
 
4.7%
1 37
 
4.3%
A 37
 
4.3%
T 36
 
4.2%
5 36
 
4.2%
Other values (23) 286
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 206
24.1%
I 52
 
6.1%
2 44
 
5.2%
4 40
 
4.7%
3 40
 
4.7%
S 40
 
4.7%
1 37
 
4.3%
A 37
 
4.3%
T 36
 
4.2%
5 36
 
4.2%
Other values (23) 286
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 206
24.1%
I 52
 
6.1%
2 44
 
5.2%
4 40
 
4.7%
3 40
 
4.7%
S 40
 
4.7%
1 37
 
4.3%
A 37
 
4.3%
T 36
 
4.2%
5 36
 
4.2%
Other values (23) 286
33.5%

fecha
Date

Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum2023-02-22 00:00:00
Maximum2023-11-14 00:00:00
2024-10-07T13:14:40.317440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:40.482838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:40.667190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.0059172
Min length5

Characters and Unicode

Total characters1184
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerano
2nd rowotoño
3rd rowinvierno
4th rowprimavera
5th rowVerano
ValueCountFrequency (%)
invierno 43
25.4%
verano 42
24.9%
otoño 42
24.9%
primavera 42
24.9%
2024-10-07T13:14:41.030308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 211
17.8%
r 169
14.3%
i 128
10.8%
n 128
10.8%
e 127
10.7%
a 126
10.6%
v 85
7.2%
V 42
 
3.5%
t 42
 
3.5%
ñ 42
 
3.5%
Other values (2) 84
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 211
17.8%
r 169
14.3%
i 128
10.8%
n 128
10.8%
e 127
10.7%
a 126
10.6%
v 85
7.2%
V 42
 
3.5%
t 42
 
3.5%
ñ 42
 
3.5%
Other values (2) 84
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 211
17.8%
r 169
14.3%
i 128
10.8%
n 128
10.8%
e 127
10.7%
a 126
10.6%
v 85
7.2%
V 42
 
3.5%
t 42
 
3.5%
ñ 42
 
3.5%
Other values (2) 84
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 211
17.8%
r 169
14.3%
i 128
10.8%
n 128
10.8%
e 127
10.7%
a 126
10.6%
v 85
7.2%
V 42
 
3.5%
t 42
 
3.5%
ñ 42
 
3.5%
Other values (2) 84
 
7.1%

tem_agua
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)39.9%
Missing31
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean19.796957
Minimum12
Maximum29.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:41.214053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12.555
Q116.075
median19
Q323
95-th percentile26.92
Maximum29.4
Range17.4
Interquartile range (IQR)6.925

Descriptive statistics

Standard deviation4.4879166
Coefficient of variation (CV)0.2266973
Kurtosis-0.96668625
Mean19.796957
Median Absolute Deviation (MAD)3.745
Skewness0.12653042
Sum2731.98
Variance20.141395
MonotonicityNot monotonic
2024-10-07T13:14:41.422656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 19
 
11.2%
22 11
 
6.5%
17 10
 
5.9%
23 9
 
5.3%
19 6
 
3.6%
21 6
 
3.6%
16 6
 
3.6%
20 5
 
3.0%
12 5
 
3.0%
24 4
 
2.4%
Other values (45) 57
33.7%
(Missing) 31
18.3%
ValueCountFrequency (%)
12 5
3.0%
12.2 1
 
0.6%
12.3 1
 
0.6%
12.6 1
 
0.6%
12.9 1
 
0.6%
13.1 1
 
0.6%
13.5 1
 
0.6%
13.7 2
 
1.2%
13.8 2
 
1.2%
14.3 1
 
0.6%
ValueCountFrequency (%)
29.4 1
 
0.6%
28.7 1
 
0.6%
28.5 1
 
0.6%
28 2
1.2%
27.8 1
 
0.6%
27.6 1
 
0.6%
26.8 1
 
0.6%
26.4 1
 
0.6%
26.1 1
 
0.6%
26 3
1.8%

tem_aire
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)15.5%
Missing27
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean18.810563
Minimum10
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:41.600147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q113
median19.5
Q324
95-th percentile28
Maximum30
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.9069223
Coefficient of variation (CV)0.31402155
Kurtosis-1.4602219
Mean18.810563
Median Absolute Deviation (MAD)5.5
Skewness0.080832879
Sum2671.1
Variance34.891732
MonotonicityNot monotonic
2024-10-07T13:14:41.762187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13 18
10.7%
25 15
8.9%
15 15
8.9%
22 13
 
7.7%
24 11
 
6.5%
14 8
 
4.7%
28 7
 
4.1%
23 7
 
4.1%
12 7
 
4.1%
11 7
 
4.1%
Other values (12) 34
20.1%
(Missing) 27
16.0%
ValueCountFrequency (%)
10 6
 
3.6%
11 7
 
4.1%
11.3 2
 
1.2%
11.5 1
 
0.6%
12 7
 
4.1%
13 18
10.7%
14 8
4.7%
15 15
8.9%
16 2
 
1.2%
18 3
 
1.8%
ValueCountFrequency (%)
30 1
 
0.6%
29 3
 
1.8%
28 7
4.1%
27 1
 
0.6%
26 5
 
3.0%
25 15
8.9%
24 11
6.5%
23 7
4.1%
22 13
7.7%
21 5
 
3.0%

od
Real number (ℝ)

MISSING 

Distinct123
Distinct (%)92.5%
Missing36
Missing (%)21.3%
Infinite0
Infinite (%)0.0%
Mean5.9421053
Minimum0.2
Maximum12.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:41.967895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.974
Q14.86
median5.94
Q37.43
95-th percentile9.558
Maximum12.62
Range12.42
Interquartile range (IQR)2.57

Descriptive statistics

Standard deviation2.3289717
Coefficient of variation (CV)0.39194386
Kurtosis0.31604852
Mean5.9421053
Median Absolute Deviation (MAD)1.22
Skewness-0.031959836
Sum790.3
Variance5.4241092
MonotonicityNot monotonic
2024-10-07T13:14:42.168201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 3
 
1.8%
5.85 2
 
1.2%
8.53 2
 
1.2%
5.4 2
 
1.2%
5.02 2
 
1.2%
6.4 2
 
1.2%
5.94 2
 
1.2%
6.33 2
 
1.2%
7.6 2
 
1.2%
4.72 1
 
0.6%
Other values (113) 113
66.9%
(Missing) 36
 
21.3%
ValueCountFrequency (%)
0.2 1
0.6%
0.6 1
0.6%
0.96 1
0.6%
1.1 1
0.6%
1.2 1
0.6%
1.22 1
0.6%
1.95 1
0.6%
1.99 1
0.6%
2.01 1
0.6%
2.2 1
0.6%
ValueCountFrequency (%)
12.62 1
0.6%
11.7 1
0.6%
11.49 1
0.6%
10.88 1
0.6%
10.02 1
0.6%
9.88 1
0.6%
9.78 1
0.6%
9.41 1
0.6%
9.3 1
0.6%
9.05 1
0.6%

ph
Real number (ℝ)

MISSING 

Distinct94
Distinct (%)75.2%
Missing44
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean7.38152
Minimum5.31
Maximum10.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:42.355607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.31
5-th percentile6.492
Q16.91
median7.3
Q37.7
95-th percentile8.7
Maximum10.5
Range5.19
Interquartile range (IQR)0.79

Descriptive statistics

Standard deviation0.77393637
Coefficient of variation (CV)0.10484783
Kurtosis2.5385223
Mean7.38152
Median Absolute Deviation (MAD)0.39
Skewness0.91047017
Sum922.69
Variance0.59897751
MonotonicityNot monotonic
2024-10-07T13:14:42.606834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.3 4
 
2.4%
7.39 3
 
1.8%
7.5 3
 
1.8%
7.48 3
 
1.8%
7.13 3
 
1.8%
6.49 2
 
1.2%
7.04 2
 
1.2%
7.05 2
 
1.2%
7.08 2
 
1.2%
6.91 2
 
1.2%
Other values (84) 99
58.6%
(Missing) 44
26.0%
ValueCountFrequency (%)
5.31 1
0.6%
5.32 1
0.6%
6 1
0.6%
6.32 1
0.6%
6.4 1
0.6%
6.49 2
1.2%
6.5 2
1.2%
6.53 1
0.6%
6.57 1
0.6%
6.58 1
0.6%
ValueCountFrequency (%)
10.5 1
0.6%
9.59 1
0.6%
9.39 1
0.6%
9.38 1
0.6%
9.18 1
0.6%
9.04 1
0.6%
8.7 2
1.2%
8.69 1
0.6%
8.67 1
0.6%
8.55 1
0.6%

olores
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Memory size470.0 B
False
140 
True
 
11
(Missing)
18 
ValueCountFrequency (%)
False 140
82.8%
True 11
 
6.5%
(Missing) 18
 
10.7%
2024-10-07T13:14:42.748735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Memory size470.0 B
False
142 
True
 
9
(Missing)
18 
ValueCountFrequency (%)
False 142
84.0%
True 9
 
5.3%
(Missing) 18
 
10.7%
2024-10-07T13:14:42.879380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Memory size470.0 B
False
146 
True
 
5
(Missing)
18 
ValueCountFrequency (%)
False 146
86.4%
True 5
 
3.0%
(Missing) 18
 
10.7%
2024-10-07T13:14:43.001555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Memory size470.0 B
False
104 
True
47 
(Missing)
18 
ValueCountFrequency (%)
False 104
61.5%
True 47
27.8%
(Missing) 18
 
10.7%
2024-10-07T13:14:43.113171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

colif_fecales_ufc_100ml
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct73
Distinct (%)50.7%
Missing25
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean7380.7007
Minimum1
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:43.281795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.06
Q1400
median1250
Q37000
95-th percentile31400
Maximum150000
Range149999
Interquartile range (IQR)6600

Descriptive statistics

Standard deviation16886.745
Coefficient of variation (CV)2.2879595
Kurtosis38.213199
Mean7380.7007
Median Absolute Deviation (MAD)1245.4
Skewness5.4067897
Sum1062820.9
Variance2.8516214 × 108
MonotonicityNot monotonic
2024-10-07T13:14:43.462495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 8
 
4.7%
4000 8
 
4.7%
2000 7
 
4.1%
15000 5
 
3.0%
600 5
 
3.0%
800 5
 
3.0%
7000 5
 
3.0%
200 5
 
3.0%
300 4
 
2.4%
400 4
 
2.4%
Other values (63) 88
52.1%
(Missing) 25
 
14.8%
ValueCountFrequency (%)
1 2
1.2%
1.1 1
 
0.6%
1.3 1
 
0.6%
2 3
1.8%
3 1
 
0.6%
3.4 1
 
0.6%
4 1
 
0.6%
4.1 1
 
0.6%
4.2 1
 
0.6%
5 1
 
0.6%
ValueCountFrequency (%)
150000 1
0.6%
81000 1
0.6%
62000 1
0.6%
52000 1
0.6%
41000 1
0.6%
40000 1
0.6%
38000 1
0.6%
32000 1
0.6%
28000 1
0.6%
23000 1
0.6%

escher_coli_ufc_100ml
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)57.6%
Missing25
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean5170.7472
Minimum1.5
Maximum170000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:43.672752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile19.15
Q1290
median845
Q33000
95-th percentile18850
Maximum170000
Range169998.5
Interquartile range (IQR)2710

Descriptive statistics

Standard deviation16931.906
Coefficient of variation (CV)3.2745568
Kurtosis65.847028
Mean5170.7472
Median Absolute Deviation (MAD)745
Skewness7.4407045
Sum744587.6
Variance2.8668943 × 108
MonotonicityNot monotonic
2024-10-07T13:14:43.867091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 9
 
5.3%
2000 7
 
4.1%
500 7
 
4.1%
100 6
 
3.6%
900 5
 
3.0%
600 5
 
3.0%
200 5
 
3.0%
800 4
 
2.4%
1100 4
 
2.4%
1000 3
 
1.8%
Other values (73) 89
52.7%
(Missing) 25
 
14.8%
ValueCountFrequency (%)
1.5 1
0.6%
2.9 1
0.6%
3 1
0.6%
3.4 1
0.6%
3.7 1
0.6%
4 1
0.6%
4.1 1
0.6%
19 1
0.6%
20 1
0.6%
21 1
0.6%
ValueCountFrequency (%)
170000 1
0.6%
75000 1
0.6%
65000 1
0.6%
38000 1
0.6%
32000 1
0.6%
20000 1
0.6%
19000 2
1.2%
18000 1
0.6%
16000 1
0.6%
15600 1
0.6%

enteroc_ufc_100ml
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)57.6%
Missing25
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean1499.8444
Minimum1
Maximum18400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:44.048615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q1130
median350
Q31040
95-th percentile7360
Maximum18400
Range18399
Interquartile range (IQR)910

Descriptive statistics

Standard deviation3167.0536
Coefficient of variation (CV)2.1115882
Kurtosis14.319774
Mean1499.8444
Median Absolute Deviation (MAD)300
Skewness3.6394308
Sum215977.59
Variance10030229
MonotonicityNot monotonic
2024-10-07T13:14:44.249377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 8
 
4.7%
200 5
 
3.0%
100 4
 
2.4%
400 4
 
2.4%
800 4
 
2.4%
3000 4
 
2.4%
1500 3
 
1.8%
140 3
 
1.8%
360 3
 
1.8%
110 3
 
1.8%
Other values (73) 103
60.9%
(Missing) 25
 
14.8%
ValueCountFrequency (%)
1 1
0.6%
1.19 1
0.6%
1.5 2
1.2%
2 1
0.6%
2.4 1
0.6%
5 1
0.6%
10 2
1.2%
15 1
0.6%
20 1
0.6%
25 1
0.6%
ValueCountFrequency (%)
18400 1
0.6%
18000 1
0.6%
16000 1
0.6%
14000 1
0.6%
10000 1
0.6%
9300 1
0.6%
9000 1
0.6%
7600 1
0.6%
6000 1
0.6%
5700 1
0.6%

nitrato_mg_l
Real number (ℝ)

MISSING 

Distinct83
Distinct (%)58.0%
Missing26
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean7.1629371
Minimum1
Maximum39.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:44.445396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.15
median5.5
Q39.75
95-th percentile18.43
Maximum39.4
Range38.4
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation5.7550306
Coefficient of variation (CV)0.80344565
Kurtosis6.5725942
Mean7.1629371
Median Absolute Deviation (MAD)2.7
Skewness2.0218039
Sum1024.3
Variance33.120377
MonotonicityNot monotonic
2024-10-07T13:14:44.629410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
7.1%
4.2 5
 
3.0%
2.7 4
 
2.4%
3.7 4
 
2.4%
2.8 4
 
2.4%
2.9 4
 
2.4%
5.3 4
 
2.4%
7.3 3
 
1.8%
4.3 3
 
1.8%
3.9 3
 
1.8%
Other values (73) 97
57.4%
(Missing) 26
 
15.4%
ValueCountFrequency (%)
1 12
7.1%
2.1 1
 
0.6%
2.3 2
 
1.2%
2.4 2
 
1.2%
2.5 2
 
1.2%
2.6 1
 
0.6%
2.7 4
 
2.4%
2.8 4
 
2.4%
2.9 4
 
2.4%
3 1
 
0.6%
ValueCountFrequency (%)
39.4 1
0.6%
23.1 1
0.6%
22.5 1
0.6%
21.5 1
0.6%
20.7 1
0.6%
20.2 1
0.6%
20 1
0.6%
18.6 1
0.6%
16.9 1
0.6%
16.7 2
1.2%

nh4_mg_l
Text

MISSING 

Distinct99
Distinct (%)65.6%
Missing18
Missing (%)10.7%
Memory size1.4 KiB
2024-10-07T13:14:45.017648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7615894
Min length1

Characters and Unicode

Total characters568
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)42.4%

Sample

1st row<0.05
2nd row0.30
3rd row0.19
4th row0.13
5th row0.25
ValueCountFrequency (%)
0.05 8
 
5.3%
0.44 4
 
2.6%
0.09 4
 
2.6%
0.06 3
 
2.0%
2.90 3
 
2.0%
1.50 3
 
2.0%
1.3 3
 
2.0%
1.4 3
 
2.0%
0.30 3
 
2.0%
0.13 3
 
2.0%
Other values (86) 114
75.5%
2024-10-07T13:14:45.814056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 154
27.1%
. 144
25.4%
1 53
 
9.3%
2 37
 
6.5%
5 34
 
6.0%
9 27
 
4.8%
4 26
 
4.6%
3 25
 
4.4%
7 23
 
4.0%
6 20
 
3.5%
Other values (2) 25
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154
27.1%
. 144
25.4%
1 53
 
9.3%
2 37
 
6.5%
5 34
 
6.0%
9 27
 
4.8%
4 26
 
4.6%
3 25
 
4.4%
7 23
 
4.0%
6 20
 
3.5%
Other values (2) 25
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154
27.1%
. 144
25.4%
1 53
 
9.3%
2 37
 
6.5%
5 34
 
6.0%
9 27
 
4.8%
4 26
 
4.6%
3 25
 
4.4%
7 23
 
4.0%
6 20
 
3.5%
Other values (2) 25
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154
27.1%
. 144
25.4%
1 53
 
9.3%
2 37
 
6.5%
5 34
 
6.0%
9 27
 
4.8%
4 26
 
4.6%
3 25
 
4.4%
7 23
 
4.0%
6 20
 
3.5%
Other values (2) 25
 
4.4%

p_total_l_mg_l
Text

MISSING 

Distinct76
Distinct (%)50.7%
Missing19
Missing (%)11.2%
Memory size1.4 KiB
2024-10-07T13:14:46.144749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9066667
Min length1

Characters and Unicode

Total characters586
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)26.0%

Sample

1st row0.26
2nd row0.16
3rd row0.57
4th row0.12
5th row0.16
ValueCountFrequency (%)
0.21 7
 
4.7%
0.20 6
 
4.0%
0.44 5
 
3.3%
0.26 5
 
3.3%
0.14 4
 
2.7%
0.22 4
 
2.7%
0.16 4
 
2.7%
0.27 4
 
2.7%
0.19 4
 
2.7%
0.23 4
 
2.7%
Other values (65) 103
68.7%
2024-10-07T13:14:46.663690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 158
27.0%
. 148
25.3%
2 58
 
9.9%
1 53
 
9.0%
4 34
 
5.8%
5 34
 
5.8%
3 29
 
4.9%
7 25
 
4.3%
6 21
 
3.6%
9 15
 
2.6%
Other values (2) 11
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 158
27.0%
. 148
25.3%
2 58
 
9.9%
1 53
 
9.0%
4 34
 
5.8%
5 34
 
5.8%
3 29
 
4.9%
7 25
 
4.3%
6 21
 
3.6%
9 15
 
2.6%
Other values (2) 11
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 158
27.0%
. 148
25.3%
2 58
 
9.9%
1 53
 
9.0%
4 34
 
5.8%
5 34
 
5.8%
3 29
 
4.9%
7 25
 
4.3%
6 21
 
3.6%
9 15
 
2.6%
Other values (2) 11
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 158
27.0%
. 148
25.3%
2 58
 
9.9%
1 53
 
9.0%
4 34
 
5.8%
5 34
 
5.8%
3 29
 
4.9%
7 25
 
4.3%
6 21
 
3.6%
9 15
 
2.6%
Other values (2) 11
 
1.9%

fosf_ortofos_mg_l
Text

MISSING 

Distinct51
Distinct (%)33.8%
Missing18
Missing (%)10.7%
Memory size1.4 KiB
2024-10-07T13:14:46.916784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length4.397351
Min length3

Characters and Unicode

Total characters664
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)11.3%

Sample

1st row<0.10
2nd row0.15
3rd row<0.20
4th row<0.20
5th row0.11
ValueCountFrequency (%)
0.20 41
26.5%
0.17 5
 
3.2%
0.18 5
 
3.2%
0.15 5
 
3.2%
0.10 5
 
3.2%
0.25 4
 
2.6%
0.23 4
 
2.6%
0.12 4
 
2.6%
0.14 4
 
2.6%
0.36 4
 
2.6%
Other values (40) 74
47.7%
2024-10-07T13:14:47.329165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 197
29.7%
. 147
22.1%
2 74
 
11.1%
1 52
 
7.8%
< 38
 
5.7%
3 29
 
4.4%
5 23
 
3.5%
4 22
 
3.3%
6 17
 
2.6%
8 10
 
1.5%
Other values (12) 55
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 197
29.7%
. 147
22.1%
2 74
 
11.1%
1 52
 
7.8%
< 38
 
5.7%
3 29
 
4.4%
5 23
 
3.5%
4 22
 
3.3%
6 17
 
2.6%
8 10
 
1.5%
Other values (12) 55
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 197
29.7%
. 147
22.1%
2 74
 
11.1%
1 52
 
7.8%
< 38
 
5.7%
3 29
 
4.4%
5 23
 
3.5%
4 22
 
3.3%
6 17
 
2.6%
8 10
 
1.5%
Other values (12) 55
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 197
29.7%
. 147
22.1%
2 74
 
11.1%
1 52
 
7.8%
< 38
 
5.7%
3 29
 
4.4%
5 23
 
3.5%
4 22
 
3.3%
6 17
 
2.6%
8 10
 
1.5%
Other values (12) 55
 
8.3%

dbo_mg_l
Text

MISSING 

Distinct65
Distinct (%)43.3%
Missing19
Missing (%)11.2%
Memory size1.4 KiB
2024-10-07T13:14:47.587255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.2266667
Min length1

Characters and Unicode

Total characters484
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)24.7%

Sample

1st row<2.0
2nd row<2.0
3rd row<2.0
4th row<2.0
5th row2.1
ValueCountFrequency (%)
2.0 43
28.7%
4.1 5
 
3.3%
3.1 4
 
2.7%
4.3 4
 
2.7%
3.8 4
 
2.7%
4 4
 
2.7%
2.1 3
 
2.0%
3.5 3
 
2.0%
2.8 3
 
2.0%
17 3
 
2.0%
Other values (54) 74
49.3%
2024-10-07T13:14:48.014691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 132
27.3%
2 78
16.1%
0 67
13.8%
< 43
 
8.9%
3 42
 
8.7%
4 31
 
6.4%
1 24
 
5.0%
5 19
 
3.9%
7 18
 
3.7%
6 12
 
2.5%
Other values (2) 18
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 132
27.3%
2 78
16.1%
0 67
13.8%
< 43
 
8.9%
3 42
 
8.7%
4 31
 
6.4%
1 24
 
5.0%
5 19
 
3.9%
7 18
 
3.7%
6 12
 
2.5%
Other values (2) 18
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 132
27.3%
2 78
16.1%
0 67
13.8%
< 43
 
8.9%
3 42
 
8.7%
4 31
 
6.4%
1 24
 
5.0%
5 19
 
3.9%
7 18
 
3.7%
6 12
 
2.5%
Other values (2) 18
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 132
27.3%
2 78
16.1%
0 67
13.8%
< 43
 
8.9%
3 42
 
8.7%
4 31
 
6.4%
1 24
 
5.0%
5 19
 
3.9%
7 18
 
3.7%
6 12
 
2.5%
Other values (2) 18
 
3.7%

dqo_mg_l
Text

MISSING 

Distinct34
Distinct (%)22.5%
Missing18
Missing (%)10.7%
Memory size1.4 KiB
2024-10-07T13:14:48.196805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.7350993
Min length2

Characters and Unicode

Total characters413
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)15.2%

Sample

1st row<30
2nd row<30
3rd row<30
4th row<30
5th row<30
ValueCountFrequency (%)
30 95
62.9%
50 8
 
5.3%
40 6
 
4.0%
59 4
 
2.6%
32 3
 
2.0%
38 3
 
2.0%
37 3
 
2.0%
52 2
 
1.3%
80 2
 
1.3%
76 2
 
1.3%
Other values (23) 23
 
15.2%
2024-10-07T13:14:48.543042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
29.1%
3 112
27.1%
< 101
24.5%
5 19
 
4.6%
6 11
 
2.7%
4 10
 
2.4%
7 10
 
2.4%
8 8
 
1.9%
1 7
 
1.7%
2 7
 
1.7%
Other values (2) 8
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120
29.1%
3 112
27.1%
< 101
24.5%
5 19
 
4.6%
6 11
 
2.7%
4 10
 
2.4%
7 10
 
2.4%
8 8
 
1.9%
1 7
 
1.7%
2 7
 
1.7%
Other values (2) 8
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120
29.1%
3 112
27.1%
< 101
24.5%
5 19
 
4.6%
6 11
 
2.7%
4 10
 
2.4%
7 10
 
2.4%
8 8
 
1.9%
1 7
 
1.7%
2 7
 
1.7%
Other values (2) 8
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120
29.1%
3 112
27.1%
< 101
24.5%
5 19
 
4.6%
6 11
 
2.7%
4 10
 
2.4%
7 10
 
2.4%
8 8
 
1.9%
1 7
 
1.7%
2 7
 
1.7%
Other values (2) 8
 
1.9%

turbiedad_ntu
Real number (ℝ)

MISSING 

Distinct55
Distinct (%)36.4%
Missing18
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean35.780132
Minimum2.9
Maximum432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:48.713618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile8.35
Q117
median25
Q345
95-th percentile85
Maximum432
Range429.1
Interquartile range (IQR)28

Descriptive statistics

Standard deviation41.161001
Coefficient of variation (CV)1.150387
Kurtosis57.576053
Mean35.780132
Median Absolute Deviation (MAD)12
Skewness6.4151961
Sum5402.8
Variance1694.228
MonotonicityNot monotonic
2024-10-07T13:14:48.903711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 8
 
4.7%
20 7
 
4.1%
17 7
 
4.1%
12 7
 
4.1%
50 7
 
4.1%
23 6
 
3.6%
21 6
 
3.6%
40 5
 
3.0%
14 5
 
3.0%
24 5
 
3.0%
Other values (45) 88
52.1%
(Missing) 18
 
10.7%
ValueCountFrequency (%)
2.9 1
0.6%
3.1 1
0.6%
3.5 1
0.6%
4 1
0.6%
6 1
0.6%
6.1 1
0.6%
6.2 1
0.6%
8 1
0.6%
8.7 1
0.6%
9.3 1
0.6%
ValueCountFrequency (%)
432 1
 
0.6%
150 1
 
0.6%
140 1
 
0.6%
120 1
 
0.6%
110 1
 
0.6%
95 2
1.2%
90 1
 
0.6%
80 1
 
0.6%
75 2
1.2%
70 3
1.8%

hidr_deriv_petr_ug_l
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing19
Missing (%)11.2%
Memory size1.4 KiB
2024-10-07T13:14:49.031825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters750
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<0.10
2nd row<0.10
3rd row<0.10
4th row<0.10
5th row<0.10
ValueCountFrequency (%)
0.10 150
100.0%
2024-10-07T13:14:49.313525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 300
40.0%
< 150
20.0%
. 150
20.0%
1 150
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 300
40.0%
< 150
20.0%
. 150
20.0%
1 150
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 300
40.0%
< 150
20.0%
. 150
20.0%
1 150
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 300
40.0%
< 150
20.0%
. 150
20.0%
1 150
20.0%

cr_total_mg_l
Text

MISSING 

Distinct16
Distinct (%)10.6%
Missing18
Missing (%)10.7%
Memory size1.4 KiB
2024-10-07T13:14:49.466390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.6556291
Min length1

Characters and Unicode

Total characters854
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)7.3%

Sample

1st row<0.005
2nd row<0.005
3rd row<0.005
4th row<0.005
5th row<0.005
ValueCountFrequency (%)
0.005 129
85.4%
0 6
 
4.0%
0.007 3
 
2.0%
0.006 2
 
1.3%
7 1
 
0.7%
0.0051 1
 
0.7%
6 1
 
0.7%
0.033 1
 
0.7%
0.0055 1
 
0.7%
0.0075 1
 
0.7%
Other values (5) 5
 
3.3%
2024-10-07T13:14:49.813500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 433
50.7%
. 143
 
16.7%
5 133
 
15.6%
< 126
 
14.8%
7 7
 
0.8%
6 4
 
0.5%
1 3
 
0.4%
3 2
 
0.2%
8 2
 
0.2%
9 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 433
50.7%
. 143
 
16.7%
5 133
 
15.6%
< 126
 
14.8%
7 7
 
0.8%
6 4
 
0.5%
1 3
 
0.4%
3 2
 
0.2%
8 2
 
0.2%
9 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 433
50.7%
. 143
 
16.7%
5 133
 
15.6%
< 126
 
14.8%
7 7
 
0.8%
6 4
 
0.5%
1 3
 
0.4%
3 2
 
0.2%
8 2
 
0.2%
9 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 433
50.7%
. 143
 
16.7%
5 133
 
15.6%
< 126
 
14.8%
7 7
 
0.8%
6 4
 
0.5%
1 3
 
0.4%
3 2
 
0.2%
8 2
 
0.2%
9 1
 
0.1%

cd_total_mg_l
Real number (ℝ)

MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean0.27225166
Minimum0.001
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-07T13:14:49.941660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.001
Q10.001
median0.001
Q31
95-th percentile1
Maximum1
Range0.999
Interquartile range (IQR)0.999

Descriptive statistics

Standard deviation0.44577905
Coefficient of variation (CV)1.6373787
Kurtosis-0.93556532
Mean0.27225166
Median Absolute Deviation (MAD)0
Skewness1.037788
Sum41.11
Variance0.19871896
MonotonicityNot monotonic
2024-10-07T13:14:50.078448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0.001 110
65.1%
1 41
 
24.3%
(Missing) 18
 
10.7%
ValueCountFrequency (%)
0.001 110
65.1%
1 41
 
24.3%
ValueCountFrequency (%)
1 41
 
24.3%
0.001 110
65.1%

clorofila_a_ug_l
Text

MISSING 

Distinct96
Distinct (%)63.2%
Missing17
Missing (%)10.1%
Memory size1.4 KiB
2024-10-07T13:14:50.392448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.7828947
Min length2

Characters and Unicode

Total characters727
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)53.3%

Sample

1st row0.87
2nd row2.56
3rd row1.02
4th row<10.00
5th row10.61
ValueCountFrequency (%)
10.00 27
 
17.3%
0.10 13
 
8.3%
en 4
 
2.6%
obra 4
 
2.6%
3.85 3
 
1.9%
2.61 3
 
1.9%
1.31 3
 
1.9%
0.87 2
 
1.3%
5.99 2
 
1.3%
2.79 2
 
1.3%
Other values (87) 93
59.6%
2024-10-07T13:14:50.898467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 147
20.2%
0 146
20.1%
1 105
14.4%
2 45
 
6.2%
< 40
 
5.5%
3 36
 
5.0%
7 36
 
5.0%
5 35
 
4.8%
4 34
 
4.7%
8 29
 
4.0%
Other values (9) 74
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 147
20.2%
0 146
20.1%
1 105
14.4%
2 45
 
6.2%
< 40
 
5.5%
3 36
 
5.0%
7 36
 
5.0%
5 35
 
4.8%
4 34
 
4.7%
8 29
 
4.0%
Other values (9) 74
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 147
20.2%
0 146
20.1%
1 105
14.4%
2 45
 
6.2%
< 40
 
5.5%
3 36
 
5.0%
7 36
 
5.0%
5 35
 
4.8%
4 34
 
4.7%
8 29
 
4.0%
Other values (9) 74
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 147
20.2%
0 146
20.1%
1 105
14.4%
2 45
 
6.2%
< 40
 
5.5%
3 36
 
5.0%
7 36
 
5.0%
5 35
 
4.8%
4 34
 
4.7%
8 29
 
4.0%
Other values (9) 74
10.2%

microcistina_ug_l
Text

MISSING 

Distinct24
Distinct (%)16.2%
Missing21
Missing (%)12.4%
Memory size1.4 KiB
2024-10-07T13:14:51.080034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.7972973
Min length3

Characters and Unicode

Total characters710
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)12.2%

Sample

1st row<0.15
2nd row<0.15
3rd row<0.15
4th row<0.15
5th row0.21
ValueCountFrequency (%)
0.15 109
73.6%
5.00 14
 
9.5%
0.16 3
 
2.0%
0.27 2
 
1.4%
0.41 2
 
1.4%
2.61 1
 
0.7%
0.21 1
 
0.7%
0.18 1
 
0.7%
0.99 1
 
0.7%
2.98 1
 
0.7%
Other values (13) 13
 
8.8%
2024-10-07T13:14:51.451781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 159
22.4%
. 148
20.8%
5 126
17.7%
1 121
17.0%
< 106
14.9%
> 14
 
2.0%
2 10
 
1.4%
9 7
 
1.0%
6 6
 
0.8%
8 5
 
0.7%
Other values (2) 8
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 159
22.4%
. 148
20.8%
5 126
17.7%
1 121
17.0%
< 106
14.9%
> 14
 
2.0%
2 10
 
1.4%
9 7
 
1.0%
6 6
 
0.8%
8 5
 
0.7%
Other values (2) 8
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 159
22.4%
. 148
20.8%
5 126
17.7%
1 121
17.0%
< 106
14.9%
> 14
 
2.0%
2 10
 
1.4%
9 7
 
1.0%
6 6
 
0.8%
8 5
 
0.7%
Other values (2) 8
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 159
22.4%
. 148
20.8%
5 126
17.7%
1 121
17.0%
< 106
14.9%
> 14
 
2.0%
2 10
 
1.4%
9 7
 
1.0%
6 6
 
0.8%
8 5
 
0.7%
Other values (2) 8
 
1.1%

ica
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)19.0%
Missing32
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean37.20438
Minimum23
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-07T13:14:51.598578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile29
Q135
median37
Q339
95-th percentile45.4
Maximum62
Range39
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.1921019
Coefficient of variation (CV)0.1395562
Kurtosis5.8587029
Mean37.20438
Median Absolute Deviation (MAD)2
Skewness1.3712111
Sum5097
Variance26.957922
MonotonicityNot monotonic
2024-10-07T13:14:51.756932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
37 22
13.0%
35 18
10.7%
40 16
9.5%
38 13
7.7%
39 12
 
7.1%
36 10
 
5.9%
34 8
 
4.7%
32 5
 
3.0%
33 5
 
3.0%
29 4
 
2.4%
Other values (16) 24
14.2%
(Missing) 32
18.9%
ValueCountFrequency (%)
23 1
 
0.6%
25 1
 
0.6%
28 2
 
1.2%
29 4
 
2.4%
30 2
 
1.2%
31 2
 
1.2%
32 5
 
3.0%
33 5
 
3.0%
34 8
4.7%
35 18
10.7%
ValueCountFrequency (%)
62 1
 
0.6%
59 1
 
0.6%
53 1
 
0.6%
49 1
 
0.6%
48 1
 
0.6%
47 2
1.2%
45 3
1.8%
44 1
 
0.6%
43 1
 
0.6%
42 1
 
0.6%

calidad_de_agua
Text

MISSING 

Distinct2
Distinct (%)1.5%
Missing32
Missing (%)18.9%
Memory size1.4 KiB
2024-10-07T13:14:51.942317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length26
Mean length25.19708
Min length15

Characters and Unicode

Total characters3452
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMuy deteriorada
2nd rowExtremadamente deteriorada
3rd rowMuy deteriorada
4th rowExtremadamente deteriorada
5th rowExtremadamente deteriorada
ValueCountFrequency (%)
deteriorada 137
50.0%
extremadamente 127
46.4%
muy 10
 
3.6%
2024-10-07T13:14:52.277909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 655
19.0%
a 528
15.3%
r 401
11.6%
d 401
11.6%
t 391
11.3%
m 254
 
7.4%
137
 
4.0%
i 137
 
4.0%
o 137
 
4.0%
E 127
 
3.7%
Other values (5) 284
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 655
19.0%
a 528
15.3%
r 401
11.6%
d 401
11.6%
t 391
11.3%
m 254
 
7.4%
137
 
4.0%
i 137
 
4.0%
o 137
 
4.0%
E 127
 
3.7%
Other values (5) 284
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 655
19.0%
a 528
15.3%
r 401
11.6%
d 401
11.6%
t 391
11.3%
m 254
 
7.4%
137
 
4.0%
i 137
 
4.0%
o 137
 
4.0%
E 127
 
3.7%
Other values (5) 284
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 655
19.0%
a 528
15.3%
r 401
11.6%
d 401
11.6%
t 391
11.3%
m 254
 
7.4%
137
 
4.0%
i 137
 
4.0%
o 137
 
4.0%
E 127
 
3.7%
Other values (5) 284
8.2%

Interactions

2024-10-07T13:14:35.086539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:19.058460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:20.613676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:22.412312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:23.876128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:25.403125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:27.012574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:28.570668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:30.309797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:31.835307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:33.225024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:35.238709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:19.200170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:20.916165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:22.541096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:24.008680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:25.590678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:27.152745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:28.715763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:30.438894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:31.966325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:33.367622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:35.375637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:19.355905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:21.069576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:22.743000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:24.136745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:25.739860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:27.288523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:28.855515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:30.588298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:32.100464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:33.519571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:35.498729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:19.476656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:21.212636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:22.857975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:24.264864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:25.869036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:27.429999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:28.990242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:30.724353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:32.219374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:33.696541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:35.615304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:19.605224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:21.345544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:22.974180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:24.360185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:25.999907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:27.559767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:29.114938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:30.823476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:32.335920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:33.927863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:35.749736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:19.741168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:21.486643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:23.108528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:24.506609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:26.147019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:27.698594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:29.263927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:31.002153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:32.466805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:34.093251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:35.890234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:19.894852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:21.626916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:23.246369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:24.642794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:26.308900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:27.833875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:29.398580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:31.149791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:32.600175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:34.358339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:36.015113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:20.033569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:21.773159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:23.368980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:24.786755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:26.449783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:28.008930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:29.529674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:31.287605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:32.737256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:34.488471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:36.164960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:20.210294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:21.927760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:23.511541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:25.041111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:26.594683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:28.158958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:29.702578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:31.430614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:32.868667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:34.633416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:36.497939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:20.320592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:22.134034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:23.627769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:25.158370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:26.739962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:28.294579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:29.822639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:31.547861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:32.979785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:34.738959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:36.614698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:20.462637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:22.259722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:23.761012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:25.274237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:26.855102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:28.422001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:30.157308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:31.688200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:33.099701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T13:14:34.865505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-07T13:14:52.395359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
cd_total_mg_lcolif_fecales_ufc_100mlcolorenteroc_ufc_100mlescher_coli_ufc_100mlespumasicamat_suspnitrato_mg_lodoloresphtem_aguatem_aireturbiedad_ntu
cd_total_mg_l1.000-0.4950.091-0.055-0.2210.000-0.1440.2540.1540.0830.1170.135-0.226-0.3020.111
colif_fecales_ufc_100ml-0.4951.0000.0000.3460.7020.396-0.2890.0520.098-0.3430.333-0.1580.1700.122-0.205
color0.0910.0001.0000.0000.0000.1700.4490.3360.2100.2180.4070.0000.0650.1290.000
enteroc_ufc_100ml-0.0550.3460.0001.0000.5640.282-0.4410.1380.261-0.2440.153-0.064-0.288-0.437-0.149
escher_coli_ufc_100ml-0.2210.7020.0000.5641.0000.402-0.4640.0000.297-0.3060.232-0.049-0.003-0.144-0.327
espumas0.0000.3960.1700.2820.4021.0000.2990.1330.0000.0000.2940.0000.0000.1810.000
ica-0.144-0.2890.449-0.441-0.4640.2991.0000.304-0.1810.3330.5610.133-0.0000.0510.213
mat_susp0.2540.0520.3360.1380.0000.1330.3041.0000.0000.1310.2100.3130.2460.1380.019
nitrato_mg_l0.1540.0980.2100.2610.2970.000-0.1810.0001.000-0.0300.2120.095-0.474-0.457-0.288
od0.083-0.3430.218-0.244-0.3060.0000.3330.131-0.0301.0000.0000.377-0.146-0.1560.412
olores0.1170.3330.4070.1530.2320.2940.5610.2100.2120.0001.0000.0000.2520.0860.000
ph0.135-0.1580.000-0.064-0.0490.0000.1330.3130.0950.3770.0001.000-0.287-0.3380.359
tem_agua-0.2260.1700.065-0.288-0.0030.000-0.0000.246-0.474-0.1460.252-0.2871.0000.823-0.022
tem_aire-0.3020.1220.129-0.437-0.1440.1810.0510.138-0.457-0.1560.086-0.3380.8231.000-0.126
turbiedad_ntu0.111-0.2050.000-0.149-0.3270.0000.2130.019-0.2880.4120.0000.359-0.022-0.1261.000

Missing values

2024-10-07T13:14:36.864749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-07T13:14:37.381440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-07T13:14:37.869001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sitioscodigofechacampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
0Canal Villanueva y Río LujánTI0012023-02-22Verano26.024.06.597.24FalseFalseFalseTrue600.0100.0130.03.9<0.050.26<0.10<2.0<3029.0<0.10<0.0050.0010.87<0.1553Muy deteriorada
1Canal Villanueva y Río LujánTI0012023-05-10otoño18.012.07.097.22FalseFalseFalseFalse3200.02200.0770.03.90.300.160.15<2.0<3045.0<0.10<0.0051.0002.56<0.1539Extremadamente deteriorada
2Canal Villanueva y Río LujánTI0012023-08-23invierno16.311.08.537.27FalseFalseFalseTrue240.0200.0300.04.20.190.57<0.20<2.0<3038.0<0.10<0.0050.0011.02<0.1548Muy deteriorada
3Canal Villanueva y Río LujánTI0012023-11-14primavera23.025.04.726.57FalseFalseFalseFalse200.0180.0290.03.30.130.12<0.20<2.0<3024.0<0.10<0.0050.001<10.00<0.1542Extremadamente deteriorada
4Río Lujan y Arroyo CaraguatáTI0062023-02-22Verano26.825.05.946.96FalseFalseFalseTrue1000.0400.01.55.20.250.160.112.1<3024.0<0.10<0.0050.00110.610.2139Extremadamente deteriorada
5Río Lujan y Arroyo CaraguatáTI0062023-05-10otoño18.012.07.127.22FalseFalseFalseTrue300.01100.0300.06.70.130.110.10<2.0<3060.0<0.10<0.0051.0000.67<0.1538Extremadamente deteriorada
6Río Lujan y Arroyo CaraguatáTI0062023-08-23invierno15.011.06.887.13FalseFalseTrueTrue15000.08300.05000.06.30.720.210.20<2.03837.0<0.10<0.0050.0011.810.1634Extremadamente deteriorada
7Río Lujan y Arroyo CaraguatáTI0062023-11-14primavera22.025.02.626.49FalseFalseFalseTrue2000.0400.0800.01.00.250.14<0.20<2.04311.0<0.10<0.0050.001<10.00<0.1535Extremadamente deteriorada
8Canal Aliviador y Río LujanTI0022023-02-22Verano27.624.06.146.88FalseFalseFalseTrue2000.01000.0100.03.52.20.410.364.1<3021.0<0.10<0.0050.00116.872.9835Extremadamente deteriorada
9Canal Aliviador y Río LujanTI0022023-05-10otoño18.012.06.167.33FalseFalseFalseFalse5.03.0200.04.20.860.210.20<2.0<3045.0<0.10<0.0051.000<0.10<0.1537Extremadamente deteriorada
sitioscodigofechacampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
159Playa La BagliardiBS0912023-11-14primavera24.020.04.537.84TrueFalseTrueTrue81000.075000.018400.01.0110.960.51238650.0<0.10<0.0050.001<10.00<0.1525Extremadamente deteriorada
160Balneario MunicipalBS0942023-02-22Verano20.024.07.538.21FalseFalseFalseTrue700.0400.0330.02.90.510.270.173.75995.0<0.100.0080.00120.52>5.00<NA>NaN
161Balneario MunicipalBS0942023-05-10otoño17.013.010.029.04FalseFalseFalseFalse1.1700.080.04.20.090.180.15<2.0<3040.0<0.10<0.0051.00041.65<0.1535Extremadamente deteriorada
162Balneario MunicipalBS0942023-11-14primavera20.020.09.788.49FalseFalseFalseFalse1000.0800.0850.01.00.330.38<0.202.27734.0<0.10<0.0050.001<10.00<0.1541Extremadamente deteriorada
163Playa La BagliardiBS0912023-08-23invierno12.210.06.338.70FalseFalseFalseFalse600.0600.0360.010.53.21.71.1047690.0<0.10<0.0050.0018.22<0.1537Extremadamente deteriorada
164Balneario MunicipalBS0942023-08-23invierno12.013.09.418.67FalseFalseFalseFalse300.0300.0150.06.60.560.560.552.36675.0<0.10<0.0050.00119.25<0.1536Extremadamente deteriorada
165Playa La BalandraBS0932023-02-22Verano20.023.05.807.47FalseFalseFalseTrue1200.0900.0720.03.20.570.790.533.45817.0<0.10<0.0050.001<0.10>5.00<NA>NaN
166Playa La BalandraBS0932023-05-10otoño18.013.05.938.35FalseFalseFalseFalse500.0500.0140.03.70.090.170.122.10<3032.0<0.10<0.0051.00054.87<0.1536Extremadamente deteriorada
167Playa La BalandraBS0932023-11-14primavera20.014.08.228.30FalseFalseFalseTrue800.0800.0360.04.10.390.21<0.20<2.048110.0<0.1000.00137.4<0.1535Extremadamente deteriorada
168Playa La BalandraBS0932023-08-23invierno12.010.08.968.09FalseFalseFalseFalse800.0500.0230.05.70.423.000.552.56750.0<0.10<0.0050.00145.070.7938Extremadamente deteriorada